{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\li_ni\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\li_ni\\Anaconda3\\lib\\site-packages\\keras_applications\\resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import keras\n",
    "from keras.layers import Dense,GlobalAveragePooling2D\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.applications import VGG16\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.resnet50 import preprocess_input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "from UnetModel import *\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#Load pre-trained model\n",
    "UnetModel.img_rows = 224\n",
    "UnetModel.img_cols = 224\n",
    "\n",
    "UnetModel.batch_size = 6\n",
    "UnetModel.batch_norm = False\n",
    "UnetModel.layer_norm = True\n",
    "\n",
    "input_shape = (UnetModel.img_rows,UnetModel.img_cols,3)\n",
    "#base_model=ResNet50(weights='imagenet',include_top=False, input_shape = input_shape) #imports the resnet model and discards the last 1000 neuron layer.\n",
    "base_model=ResNet50(weights='imagenet',include_top=False, input_shape = input_shape) #imports the resnet model and discards the last 1000 neuron layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\li_ni\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "for layer in base_model.layers:\n",
    "    layer.trainable = True\n",
    "    \n",
    "x = base_model.output\n",
    "\n",
    "bottom  = double_block(x, 1024, prefix='bottom')\n",
    "\n",
    "# 14 x 14 block5\n",
    "left_block5 = base_model.get_layer('activation_40').output\n",
    "right_conv5 = up_sampling_block(bottom, left_block5, 512, 'right_conv5')\n",
    "\n",
    "# 28 x 28 block4\n",
    "left_block4 = base_model.get_layer('activation_22').output\n",
    "right_conv4 = up_sampling_block(right_conv5, left_block4, 256, 'right_conv4')\n",
    "\n",
    "# 56 x 56 block3\n",
    "left_block3 = base_model.get_layer('activation_10').output\n",
    "right_conv3 = up_sampling_block(right_conv4, left_block3, 128, 'right_conv3')\n",
    "\n",
    "# 112 x 112 block2\n",
    "left_block2 = base_model.get_layer('activation_1').output\n",
    "right_conv2 = up_sampling_block(right_conv3, left_block2, 64, 'right_conv2')\n",
    "\n",
    "#Resnet doesn't have 224 x 224 layer conv, use first conv layer in VGG16 instead\n",
    "vgg = VGG16(input_shape=input_shape, input_tensor=base_model.input, include_top=False)\n",
    "for l in vgg.layers:\n",
    "    l.trainable = True\n",
    "vgg_first_conv = vgg.get_layer(\"block1_conv2\").output\n",
    "right_conv1 = up_sampling_block(right_conv2, vgg_first_conv, 32, 'right_conv1')\n",
    "right_dropout1 = SpatialDropout2D(0.2)(right_conv1)\n",
    "\n",
    "#Sigmoid\n",
    "output = Conv2D(1, (1, 1), activation='sigmoid')(right_dropout1)\n",
    "model = Model(inputs=base_model.input, outputs=output)\n",
    "\n",
    "model.compile(optimizer=Adam(lr=1e-4, decay = 0.1), loss=jaccard_coef_loss, metrics=[jaccard_coef])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydot\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='resnet_model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\li_ni\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/100\n",
      "Found 519 images belonging to 1 classes.\n",
      "Found 1555 images belonging to 1 classes.\n",
      "Found 519 images belonging to 1 classes.\n",
      "Found 1555 images belonging to 1 classes.\n",
      "300/300 [==============================] - 563s 2s/step - loss: -0.4560 - jaccard_coef: 0.4560 - val_loss: -0.5915 - val_jaccard_coef: 0.5915\n",
      "\n",
      "Epoch 00001: val_jaccard_coef improved from -inf to 0.59152, saving model to unet/20190602/unet_lesion_20190602_001-0.59152.hdf5\n",
      "Epoch 2/100\n",
      "300/300 [==============================] - 533s 2s/step - loss: -0.5879 - jaccard_coef: 0.5879 - val_loss: -0.6260 - val_jaccard_coef: 0.6260\n",
      "\n",
      "Epoch 00002: val_jaccard_coef improved from 0.59152 to 0.62599, saving model to unet/20190602/unet_lesion_20190602_002-0.62599.hdf5\n",
      "Epoch 3/100\n",
      "300/300 [==============================] - 546s 2s/step - loss: -0.6205 - jaccard_coef: 0.6205 - val_loss: -0.6474 - val_jaccard_coef: 0.6474\n",
      "\n",
      "Epoch 00003: val_jaccard_coef improved from 0.62599 to 0.64741, saving model to unet/20190602/unet_lesion_20190602_003-0.64741.hdf5\n",
      "Epoch 4/100\n",
      "300/300 [==============================] - 535s 2s/step - loss: -0.6325 - jaccard_coef: 0.6325 - val_loss: -0.6634 - val_jaccard_coef: 0.6634\n",
      "\n",
      "Epoch 00004: val_jaccard_coef improved from 0.64741 to 0.66340, saving model to unet/20190602/unet_lesion_20190602_004-0.66340.hdf5\n",
      "Epoch 5/100\n",
      "300/300 [==============================] - 553s 2s/step - loss: -0.6563 - jaccard_coef: 0.6563 - val_loss: -0.6820 - val_jaccard_coef: 0.6820\n",
      "\n",
      "Epoch 00005: val_jaccard_coef improved from 0.66340 to 0.68200, saving model to unet/20190602/unet_lesion_20190602_005-0.68200.hdf5\n",
      "Epoch 6/100\n",
      "300/300 [==============================] - 541s 2s/step - loss: -0.6550 - jaccard_coef: 0.6550 - val_loss: -0.6771 - val_jaccard_coef: 0.6771\n",
      "\n",
      "Epoch 00006: val_jaccard_coef did not improve from 0.68200\n",
      "Epoch 7/100\n",
      "300/300 [==============================] - 543s 2s/step - loss: -0.6729 - jaccard_coef: 0.6729 - val_loss: -0.6895 - val_jaccard_coef: 0.6895\n",
      "\n",
      "Epoch 00007: val_jaccard_coef improved from 0.68200 to 0.68948, saving model to unet/20190602/unet_lesion_20190602_007-0.68948.hdf5\n",
      "Epoch 8/100\n",
      "300/300 [==============================] - 541s 2s/step - loss: -0.6740 - jaccard_coef: 0.6740 - val_loss: -0.6908 - val_jaccard_coef: 0.6908\n",
      "\n",
      "Epoch 00008: val_jaccard_coef improved from 0.68948 to 0.69078, saving model to unet/20190602/unet_lesion_20190602_008-0.69078.hdf5\n",
      "Epoch 9/100\n",
      "300/300 [==============================] - 534s 2s/step - loss: -0.6809 - jaccard_coef: 0.6809 - val_loss: -0.6933 - val_jaccard_coef: 0.6933\n",
      "\n",
      "Epoch 00009: val_jaccard_coef improved from 0.69078 to 0.69334, saving model to unet/20190602/unet_lesion_20190602_009-0.69334.hdf5\n",
      "Epoch 10/100\n",
      "300/300 [==============================] - 521s 2s/step - loss: -0.6816 - jaccard_coef: 0.6816 - val_loss: -0.6770 - val_jaccard_coef: 0.6770\n",
      "\n",
      "Epoch 00010: val_jaccard_coef did not improve from 0.69334\n",
      "Epoch 11/100\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.6912 - jaccard_coef: 0.6912 - val_loss: -0.6964 - val_jaccard_coef: 0.6964\n",
      "\n",
      "Epoch 00011: val_jaccard_coef improved from 0.69334 to 0.69641, saving model to unet/20190602/unet_lesion_20190602_011-0.69641.hdf5\n",
      "Epoch 12/100\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.6883 - jaccard_coef: 0.6883 - val_loss: -0.6946 - val_jaccard_coef: 0.6946\n",
      "\n",
      "Epoch 00012: val_jaccard_coef did not improve from 0.69641\n",
      "Epoch 13/100\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.6883 - jaccard_coef: 0.6883 - val_loss: -0.6877 - val_jaccard_coef: 0.6877\n",
      "\n",
      "Epoch 00013: val_jaccard_coef did not improve from 0.69641\n",
      "Epoch 14/100\n",
      "300/300 [==============================] - 498s 2s/step - loss: -0.6958 - jaccard_coef: 0.6958 - val_loss: -0.7054 - val_jaccard_coef: 0.7054\n",
      "\n",
      "Epoch 00014: val_jaccard_coef improved from 0.69641 to 0.70541, saving model to unet/20190602/unet_lesion_20190602_014-0.70541.hdf5\n",
      "Epoch 15/100\n",
      "300/300 [==============================] - 522s 2s/step - loss: -0.6937 - jaccard_coef: 0.6937 - val_loss: -0.6981 - val_jaccard_coef: 0.6981\n",
      "\n",
      "Epoch 00015: val_jaccard_coef did not improve from 0.70541\n",
      "Epoch 16/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7065 - jaccard_coef: 0.7065 - val_loss: -0.6917 - val_jaccard_coef: 0.6917\n",
      "\n",
      "Epoch 00016: val_jaccard_coef did not improve from 0.70541\n",
      "Epoch 17/100\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.7017 - jaccard_coef: 0.7017 - val_loss: -0.7040 - val_jaccard_coef: 0.7040\n",
      "\n",
      "Epoch 00017: val_jaccard_coef did not improve from 0.70541\n",
      "Epoch 18/100\n",
      "300/300 [==============================] - 500s 2s/step - loss: -0.7048 - jaccard_coef: 0.7048 - val_loss: -0.7043 - val_jaccard_coef: 0.7043\n",
      "\n",
      "Epoch 00018: val_jaccard_coef did not improve from 0.70541\n",
      "Epoch 19/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7048 - jaccard_coef: 0.7048 - val_loss: -0.7118 - val_jaccard_coef: 0.7118\n",
      "\n",
      "Epoch 00019: val_jaccard_coef improved from 0.70541 to 0.71175, saving model to unet/20190602/unet_lesion_20190602_019-0.71175.hdf5\n",
      "Epoch 20/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7112 - jaccard_coef: 0.7112 - val_loss: -0.7003 - val_jaccard_coef: 0.7003\n",
      "\n",
      "Epoch 00020: val_jaccard_coef did not improve from 0.71175\n",
      "Epoch 21/100\n",
      "300/300 [==============================] - 501s 2s/step - loss: -0.7099 - jaccard_coef: 0.7099 - val_loss: -0.7106 - val_jaccard_coef: 0.7106\n",
      "\n",
      "Epoch 00021: val_jaccard_coef did not improve from 0.71175\n",
      "Epoch 22/100\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.7118 - jaccard_coef: 0.7118 - val_loss: -0.7023 - val_jaccard_coef: 0.7023\n",
      "\n",
      "Epoch 00022: val_jaccard_coef did not improve from 0.71175\n",
      "Epoch 23/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7101 - jaccard_coef: 0.7101 - val_loss: -0.7091 - val_jaccard_coef: 0.7091\n",
      "\n",
      "Epoch 00023: val_jaccard_coef did not improve from 0.71175\n",
      "Epoch 24/100\n",
      "300/300 [==============================] - 504s 2s/step - loss: -0.7109 - jaccard_coef: 0.7109 - val_loss: -0.7056 - val_jaccard_coef: 0.7056\n",
      "\n",
      "Epoch 00024: val_jaccard_coef did not improve from 0.71175\n",
      "Epoch 25/100\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.7163 - jaccard_coef: 0.7163 - val_loss: -0.7186 - val_jaccard_coef: 0.7186\n",
      "\n",
      "Epoch 00025: val_jaccard_coef improved from 0.71175 to 0.71863, saving model to unet/20190602/unet_lesion_20190602_025-0.71863.hdf5\n",
      "Epoch 26/100\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.7199 - jaccard_coef: 0.7199 - val_loss: -0.7034 - val_jaccard_coef: 0.7034\n",
      "\n",
      "Epoch 00026: val_jaccard_coef did not improve from 0.71863\n",
      "Epoch 27/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7195 - jaccard_coef: 0.7195 - val_loss: -0.7119 - val_jaccard_coef: 0.7119\n",
      "\n",
      "Epoch 00027: val_jaccard_coef did not improve from 0.71863\n",
      "Epoch 28/100\n",
      "300/300 [==============================] - 540s 2s/step - loss: -0.7285 - jaccard_coef: 0.7285 - val_loss: -0.7075 - val_jaccard_coef: 0.7075\n",
      "\n",
      "Epoch 00028: val_jaccard_coef did not improve from 0.71863\n",
      "Epoch 29/100\n",
      "300/300 [==============================] - 547s 2s/step - loss: -0.7148 - jaccard_coef: 0.7148 - val_loss: -0.7193 - val_jaccard_coef: 0.7193\n",
      "\n",
      "Epoch 00029: val_jaccard_coef improved from 0.71863 to 0.71935, saving model to unet/20190602/unet_lesion_20190602_029-0.71935.hdf5\n",
      "Epoch 30/100\n",
      "300/300 [==============================] - 541s 2s/step - loss: -0.7251 - jaccard_coef: 0.7251 - val_loss: -0.7128 - val_jaccard_coef: 0.7128\n",
      "\n",
      "Epoch 00030: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 31/100\n",
      "300/300 [==============================] - 533s 2s/step - loss: -0.7226 - jaccard_coef: 0.7226 - val_loss: -0.7159 - val_jaccard_coef: 0.7159\n",
      "\n",
      "Epoch 00031: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 32/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7271 - jaccard_coef: 0.7271 - val_loss: -0.7084 - val_jaccard_coef: 0.7084\n",
      "\n",
      "Epoch 00032: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 33/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300/300 [==============================] - 503s 2s/step - loss: -0.7232 - jaccard_coef: 0.7232 - val_loss: -0.7107 - val_jaccard_coef: 0.7107\n",
      "\n",
      "Epoch 00033: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 34/100\n",
      "300/300 [==============================] - 522s 2s/step - loss: -0.7287 - jaccard_coef: 0.7287 - val_loss: -0.7163 - val_jaccard_coef: 0.7163\n",
      "\n",
      "Epoch 00034: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 35/100\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.7254 - jaccard_coef: 0.7254 - val_loss: -0.7169 - val_jaccard_coef: 0.7169\n",
      "\n",
      "Epoch 00035: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 36/100\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.7304 - jaccard_coef: 0.7304 - val_loss: -0.7147 - val_jaccard_coef: 0.7147\n",
      "\n",
      "Epoch 00036: val_jaccard_coef did not improve from 0.71935\n",
      "Epoch 37/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7298 - jaccard_coef: 0.7298 - val_loss: -0.7230 - val_jaccard_coef: 0.7230\n",
      "\n",
      "Epoch 00037: val_jaccard_coef improved from 0.71935 to 0.72296, saving model to unet/20190602/unet_lesion_20190602_037-0.72296.hdf5\n",
      "Epoch 38/100\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.7279 - jaccard_coef: 0.7279 - val_loss: -0.7135 - val_jaccard_coef: 0.7135\n",
      "\n",
      "Epoch 00038: val_jaccard_coef did not improve from 0.72296\n",
      "Epoch 39/100\n",
      "300/300 [==============================] - 508s 2s/step - loss: -0.7271 - jaccard_coef: 0.7271 - val_loss: -0.7193 - val_jaccard_coef: 0.7193\n",
      "\n",
      "Epoch 00039: val_jaccard_coef did not improve from 0.72296\n",
      "Epoch 40/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7354 - jaccard_coef: 0.7354 - val_loss: -0.7083 - val_jaccard_coef: 0.7083\n",
      "\n",
      "Epoch 00040: val_jaccard_coef did not improve from 0.72296\n",
      "Epoch 41/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7307 - jaccard_coef: 0.7307 - val_loss: -0.7246 - val_jaccard_coef: 0.7246\n",
      "\n",
      "Epoch 00041: val_jaccard_coef improved from 0.72296 to 0.72462, saving model to unet/20190602/unet_lesion_20190602_041-0.72462.hdf5\n",
      "Epoch 42/100\n",
      "300/300 [==============================] - 524s 2s/step - loss: -0.7321 - jaccard_coef: 0.7321 - val_loss: -0.7138 - val_jaccard_coef: 0.7138\n",
      "\n",
      "Epoch 00042: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 43/100\n",
      "300/300 [==============================] - 496s 2s/step - loss: -0.7333 - jaccard_coef: 0.7333 - val_loss: -0.7211 - val_jaccard_coef: 0.7211\n",
      "\n",
      "Epoch 00043: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 44/100\n",
      "300/300 [==============================] - 531s 2s/step - loss: -0.7321 - jaccard_coef: 0.7321 - val_loss: -0.7174 - val_jaccard_coef: 0.7174\n",
      "\n",
      "Epoch 00044: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 45/100\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.7315 - jaccard_coef: 0.7315 - val_loss: -0.7215 - val_jaccard_coef: 0.7215\n",
      "\n",
      "Epoch 00045: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 46/100\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.7333 - jaccard_coef: 0.7333 - val_loss: -0.7189 - val_jaccard_coef: 0.7189\n",
      "\n",
      "Epoch 00046: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 47/100\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.7338 - jaccard_coef: 0.7338 - val_loss: -0.7184 - val_jaccard_coef: 0.7184\n",
      "\n",
      "Epoch 00047: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 48/100\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.7366 - jaccard_coef: 0.7366 - val_loss: -0.7175 - val_jaccard_coef: 0.7175\n",
      "\n",
      "Epoch 00048: val_jaccard_coef did not improve from 0.72462\n",
      "Epoch 49/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7323 - jaccard_coef: 0.7323 - val_loss: -0.7284 - val_jaccard_coef: 0.7284\n",
      "\n",
      "Epoch 00049: val_jaccard_coef improved from 0.72462 to 0.72845, saving model to unet/20190602/unet_lesion_20190602_049-0.72845.hdf5\n",
      "Epoch 50/100\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.7381 - jaccard_coef: 0.7381 - val_loss: -0.7279 - val_jaccard_coef: 0.7279\n",
      "\n",
      "Epoch 00050: val_jaccard_coef did not improve from 0.72845\n",
      "Epoch 51/100\n",
      "300/300 [==============================] - 520s 2s/step - loss: -0.7361 - jaccard_coef: 0.7361 - val_loss: -0.7149 - val_jaccard_coef: 0.7149\n",
      "\n",
      "Epoch 00051: val_jaccard_coef did not improve from 0.72845\n",
      "Epoch 52/100\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.7348 - jaccard_coef: 0.7348 - val_loss: -0.7183 - val_jaccard_coef: 0.7183\n",
      "\n",
      "Epoch 00052: val_jaccard_coef did not improve from 0.72845\n",
      "Epoch 53/100\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.7355 - jaccard_coef: 0.7355 - val_loss: -0.7165 - val_jaccard_coef: 0.7165\n",
      "\n",
      "Epoch 00053: val_jaccard_coef did not improve from 0.72845\n",
      "Epoch 54/100\n",
      "300/300 [==============================] - 530s 2s/step - loss: -0.7378 - jaccard_coef: 0.7378 - val_loss: -0.7307 - val_jaccard_coef: 0.7307\n",
      "\n",
      "Epoch 00054: val_jaccard_coef improved from 0.72845 to 0.73066, saving model to unet/20190602/unet_lesion_20190602_054-0.73066.hdf5\n",
      "Epoch 55/100\n",
      "300/300 [==============================] - 499s 2s/step - loss: -0.7344 - jaccard_coef: 0.7344 - val_loss: -0.7234 - val_jaccard_coef: 0.7234\n",
      "\n",
      "Epoch 00055: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 56/100\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.7395 - jaccard_coef: 0.7395 - val_loss: -0.7243 - val_jaccard_coef: 0.7243\n",
      "\n",
      "Epoch 00056: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 57/100\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.7368 - jaccard_coef: 0.7368 - val_loss: -0.7229 - val_jaccard_coef: 0.7229\n",
      "\n",
      "Epoch 00057: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 58/100\n",
      "300/300 [==============================] - 543s 2s/step - loss: -0.7388 - jaccard_coef: 0.7388 - val_loss: -0.7159 - val_jaccard_coef: 0.7159\n",
      "\n",
      "Epoch 00058: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 59/100\n",
      "300/300 [==============================] - 546s 2s/step - loss: -0.7338 - jaccard_coef: 0.7338 - val_loss: -0.7198 - val_jaccard_coef: 0.7198\n",
      "\n",
      "Epoch 00059: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 60/100\n",
      "300/300 [==============================] - 544s 2s/step - loss: -0.7439 - jaccard_coef: 0.7439 - val_loss: -0.7200 - val_jaccard_coef: 0.7200\n",
      "\n",
      "Epoch 00060: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 61/100\n",
      "300/300 [==============================] - 528s 2s/step - loss: -0.7468 - jaccard_coef: 0.7468 - val_loss: -0.7258 - val_jaccard_coef: 0.7258\n",
      "\n",
      "Epoch 00061: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 62/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7382 - jaccard_coef: 0.7382 - val_loss: -0.7240 - val_jaccard_coef: 0.7240\n",
      "\n",
      "Epoch 00062: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 63/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7460 - jaccard_coef: 0.7460 - val_loss: -0.7293 - val_jaccard_coef: 0.7293\n",
      "\n",
      "Epoch 00063: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 64/100\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.7327 - jaccard_coef: 0.7327 - val_loss: -0.7204 - val_jaccard_coef: 0.7204\n",
      "\n",
      "Epoch 00064: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 65/100\n",
      "300/300 [==============================] - 504s 2s/step - loss: -0.7422 - jaccard_coef: 0.7422 - val_loss: -0.7223 - val_jaccard_coef: 0.7223\n",
      "\n",
      "Epoch 00065: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 66/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7456 - jaccard_coef: 0.7456 - val_loss: -0.7253 - val_jaccard_coef: 0.7253\n",
      "\n",
      "Epoch 00066: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 67/100\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.7375 - jaccard_coef: 0.7375 - val_loss: -0.7282 - val_jaccard_coef: 0.7282\n",
      "\n",
      "Epoch 00067: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 68/100\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.7449 - jaccard_coef: 0.7449 - val_loss: -0.7224 - val_jaccard_coef: 0.7224\n",
      "\n",
      "Epoch 00068: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 69/100\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.7405 - jaccard_coef: 0.7405 - val_loss: -0.7276 - val_jaccard_coef: 0.7276\n",
      "\n",
      "Epoch 00069: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 70/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300/300 [==============================] - 513s 2s/step - loss: -0.7449 - jaccard_coef: 0.7449 - val_loss: -0.7288 - val_jaccard_coef: 0.7288\n",
      "\n",
      "Epoch 00070: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 71/100\n",
      "300/300 [==============================] - 520s 2s/step - loss: -0.7482 - jaccard_coef: 0.7482 - val_loss: -0.7211 - val_jaccard_coef: 0.7211\n",
      "\n",
      "Epoch 00071: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 72/100\n",
      "300/300 [==============================] - 505s 2s/step - loss: -0.7420 - jaccard_coef: 0.7420 - val_loss: -0.7189 - val_jaccard_coef: 0.7189\n",
      "\n",
      "Epoch 00072: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 73/100\n",
      "300/300 [==============================] - 523s 2s/step - loss: -0.7403 - jaccard_coef: 0.7403 - val_loss: -0.7243 - val_jaccard_coef: 0.7243\n",
      "\n",
      "Epoch 00073: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 74/100\n",
      "300/300 [==============================] - 503s 2s/step - loss: -0.7433 - jaccard_coef: 0.7433 - val_loss: -0.7249 - val_jaccard_coef: 0.7249\n",
      "\n",
      "Epoch 00074: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 75/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7452 - jaccard_coef: 0.7452 - val_loss: -0.7281 - val_jaccard_coef: 0.7281\n",
      "\n",
      "Epoch 00075: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 76/100\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.7433 - jaccard_coef: 0.7433 - val_loss: -0.7252 - val_jaccard_coef: 0.7252\n",
      "\n",
      "Epoch 00076: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 77/100\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.7384 - jaccard_coef: 0.7384 - val_loss: -0.7260 - val_jaccard_coef: 0.7260\n",
      "\n",
      "Epoch 00077: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 78/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7502 - jaccard_coef: 0.7502 - val_loss: -0.7202 - val_jaccard_coef: 0.7202\n",
      "\n",
      "Epoch 00078: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 79/100\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.7471 - jaccard_coef: 0.7471 - val_loss: -0.7274 - val_jaccard_coef: 0.7274\n",
      "\n",
      "Epoch 00079: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 80/100\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.7453 - jaccard_coef: 0.7453 - val_loss: -0.7199 - val_jaccard_coef: 0.7199\n",
      "\n",
      "Epoch 00080: val_jaccard_coef did not improve from 0.73066\n",
      "Epoch 81/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7511 - jaccard_coef: 0.7511 - val_loss: -0.7330 - val_jaccard_coef: 0.7330\n",
      "\n",
      "Epoch 00081: val_jaccard_coef improved from 0.73066 to 0.73304, saving model to unet/20190602/unet_lesion_20190602_081-0.73304.hdf5\n",
      "Epoch 82/100\n",
      "300/300 [==============================] - 523s 2s/step - loss: -0.7492 - jaccard_coef: 0.7492 - val_loss: -0.7231 - val_jaccard_coef: 0.7231\n",
      "\n",
      "Epoch 00082: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 83/100\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.7515 - jaccard_coef: 0.7515 - val_loss: -0.7310 - val_jaccard_coef: 0.7310\n",
      "\n",
      "Epoch 00083: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 84/100\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.7443 - jaccard_coef: 0.7443 - val_loss: -0.7275 - val_jaccard_coef: 0.7275\n",
      "\n",
      "Epoch 00084: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 85/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7469 - jaccard_coef: 0.7469 - val_loss: -0.7227 - val_jaccard_coef: 0.7227\n",
      "\n",
      "Epoch 00085: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 86/100\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.7504 - jaccard_coef: 0.7504 - val_loss: -0.7160 - val_jaccard_coef: 0.7160\n",
      "\n",
      "Epoch 00086: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 87/100\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.7486 - jaccard_coef: 0.7486 - val_loss: -0.7240 - val_jaccard_coef: 0.7240\n",
      "\n",
      "Epoch 00087: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 88/100\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.7510 - jaccard_coef: 0.7510 - val_loss: -0.7200 - val_jaccard_coef: 0.7200\n",
      "\n",
      "Epoch 00088: val_jaccard_coef did not improve from 0.73304\n",
      "Epoch 89/100\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.7453 - jaccard_coef: 0.7453 - val_loss: -0.7345 - val_jaccard_coef: 0.7345\n",
      "\n",
      "Epoch 00089: val_jaccard_coef improved from 0.73304 to 0.73447, saving model to unet/20190602/unet_lesion_20190602_089-0.73447.hdf5\n",
      "Epoch 90/100\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.7475 - jaccard_coef: 0.7475 - val_loss: -0.7224 - val_jaccard_coef: 0.7224\n",
      "\n",
      "Epoch 00090: val_jaccard_coef did not improve from 0.73447\n",
      "Epoch 91/100\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.7482 - jaccard_coef: 0.7482 - val_loss: -0.7350 - val_jaccard_coef: 0.7350\n",
      "\n",
      "Epoch 00091: val_jaccard_coef improved from 0.73447 to 0.73500, saving model to unet/20190602/unet_lesion_20190602_091-0.73500.hdf5\n",
      "Epoch 92/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7491 - jaccard_coef: 0.7491 - val_loss: -0.7288 - val_jaccard_coef: 0.7288\n",
      "\n",
      "Epoch 00092: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 93/100\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.7515 - jaccard_coef: 0.7515 - val_loss: -0.7287 - val_jaccard_coef: 0.7287\n",
      "\n",
      "Epoch 00093: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 94/100\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.7495 - jaccard_coef: 0.7495 - val_loss: -0.7246 - val_jaccard_coef: 0.7246\n",
      "\n",
      "Epoch 00094: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 95/100\n",
      "300/300 [==============================] - 505s 2s/step - loss: -0.7537 - jaccard_coef: 0.7537 - val_loss: -0.7297 - val_jaccard_coef: 0.7297\n",
      "\n",
      "Epoch 00095: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 96/100\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.7463 - jaccard_coef: 0.7463 - val_loss: -0.7319 - val_jaccard_coef: 0.7319\n",
      "\n",
      "Epoch 00096: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 97/100\n",
      "300/300 [==============================] - 520s 2s/step - loss: -0.7524 - jaccard_coef: 0.7524 - val_loss: -0.7296 - val_jaccard_coef: 0.7296\n",
      "\n",
      "Epoch 00097: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 98/100\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.7514 - jaccard_coef: 0.7514 - val_loss: -0.7190 - val_jaccard_coef: 0.7190\n",
      "\n",
      "Epoch 00098: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 99/100\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.7511 - jaccard_coef: 0.7511 - val_loss: -0.7300 - val_jaccard_coef: 0.7300\n",
      "\n",
      "Epoch 00099: val_jaccard_coef did not improve from 0.73500\n",
      "Epoch 100/100\n",
      "300/300 [==============================] - 526s 2s/step - loss: -0.7458 - jaccard_coef: 0.7458 - val_loss: -0.7314 - val_jaccard_coef: 0.7314\n",
      "\n",
      "Epoch 00100: val_jaccard_coef did not improve from 0.73500\n"
     ]
    }
   ],
   "source": [
    "myGene = trainGenerator(batch_size,'data/train','images','masks',data_val_gen_args, target_size= (UnetModel.img_rows,UnetModel.img_rows))\n",
    "myValGene = validationGenerator(batch_size,'data/val','images','masks',data_val_gen_args, target_size= (UnetModel.img_rows,UnetModel.img_rows))\n",
    "\n",
    "iterations = 100\n",
    "# Train head\n",
    "history = model.fit_generator(\n",
    "    myGene,\n",
    "    steps_per_epoch = 300, \n",
    "    epochs=iterations,\n",
    "    callbacks=[model_checkpoint],\n",
    "    validation_data=myValGene,\n",
    "    validation_steps=100)\n",
    "\n",
    "model.save(os.path.join(output_dir, 'transfer_learning_iter_100'.format(iterations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.5915222121543021, -0.6259910709893883, -0.6474091389670444, -0.6634038814048672, -0.6819997528689591, -0.6771084077693709, -0.6894845197899173, -0.6907759464565834, -0.6933389000557176, -0.6769942251881164, -0.6964115467502843, -0.694646249164888, -0.6877200991364579, -0.7054136850015081, -0.6981406972635931, -0.691675427571014, -0.7040011601232404, -0.7043364548203933, -0.7117522257057267, -0.7002553071208935, -0.7106360025478132, -0.7022697918079607, -0.7091477237754131, -0.7056073354716277, -0.7186306843805552, -0.7033850170260099, -0.7118743627962439, -0.7075294211282203, -0.7193466664558679, -0.7128074537569554, -0.7158993087222228, -0.7083675145503864, -0.7107267945855107, -0.716252782730141, -0.7169444075780897, -0.7147336841827661, -0.7229608492036561, -0.7135399963388491, -0.7192812729720495, -0.7082620919050284, -0.7246242806766973, -0.7137708171228667, -0.7211328661022474, -0.7173993359858067, -0.7214858600841695, -0.7188730991665443, -0.7183646352002115, -0.7174912424542796, -0.7284488860686221, -0.7278913915456839, -0.7148632676757161, -0.7182834903199469, -0.7165385637451057, -0.7306558703533327, -0.7233800196168411, -0.7242766376116767, -0.722918407102326, -0.7158898219990371, -0.7197982399307903, -0.7199877272898229, -0.7258156729165954, -0.723995092825674, -0.7293227506642366, -0.7204461897437896, -0.7223291214387021, -0.7252934584066496, -0.7281739470934627, -0.7224106120703807, -0.7276277182689265, -0.7287589098939944, -0.721089290913625, -0.7189234525115047, -0.7243051406127125, -0.7249321997767747, -0.7280745467348914, -0.725167913053503, -0.7260199108315473, -0.7202248597264889, -0.7273640998044805, -0.7199159938486377, -0.7330387017943643, -0.7230615711691392, -0.7309734012613345, -0.7275126594394895, -0.7226852627854851, -0.7160225992825762, -0.7239650423177565, -0.7200266434319654, -0.734468158465534, -0.722365769609135, -0.735000137408175, -0.7287942800090541, -0.7286654327382993, -0.7245700848524017, -0.7296630281898844, -0.7318918068504813, -0.7296434805620855, -0.7189976596952083, -0.730047393983333, -0.7314322722617106]\n"
     ]
    }
   ],
   "source": [
    "print(history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bottom1_conv (Conv2D)           (None, 7, 7, 1024)   18875392    activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_1 (LayerNor (None, 7, 7, 1024)   2048        bottom1_conv[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bottom1_activation (LeakyReLU)  (None, 7, 7, 1024)   0           layer_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "bottom2_conv (Conv2D)           (None, 7, 7, 1024)   9438208     bottom1_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_2 (LayerNor (None, 7, 7, 1024)   2048        bottom2_conv[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bottom2_activation (LeakyReLU)  (None, 7, 7, 1024)   0           layer_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 14, 14, 1024) 0           bottom2_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 14, 14, 2048) 0           up_sampling2d_1[0][0]            \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "right_conv51_conv (Conv2D)      (None, 14, 14, 512)  9437696     concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_3 (LayerNor (None, 14, 14, 512)  1024        right_conv51_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv51_activation (LeakyR (None, 14, 14, 512)  0           layer_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "right_conv52_conv (Conv2D)      (None, 14, 14, 512)  2359808     right_conv51_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_4 (LayerNor (None, 14, 14, 512)  1024        right_conv52_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv52_activation (LeakyR (None, 14, 14, 512)  0           layer_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 28, 28, 512)  0           right_conv52_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 28, 28, 1024) 0           up_sampling2d_2[0][0]            \n",
      "                                                                 activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "right_conv41_conv (Conv2D)      (None, 28, 28, 256)  2359552     concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_5 (LayerNor (None, 28, 28, 256)  512         right_conv41_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv41_activation (LeakyR (None, 28, 28, 256)  0           layer_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "right_conv42_conv (Conv2D)      (None, 28, 28, 256)  590080      right_conv41_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_6 (LayerNor (None, 28, 28, 256)  512         right_conv42_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv42_activation (LeakyR (None, 28, 28, 256)  0           layer_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 56, 56, 256)  0           right_conv42_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 56, 56, 512)  0           up_sampling2d_3[0][0]            \n",
      "                                                                 activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "right_conv31_conv (Conv2D)      (None, 56, 56, 128)  589952      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_7 (LayerNor (None, 56, 56, 128)  256         right_conv31_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv31_activation (LeakyR (None, 56, 56, 128)  0           layer_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "right_conv32_conv (Conv2D)      (None, 56, 56, 128)  147584      right_conv31_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_8 (LayerNor (None, 56, 56, 128)  256         right_conv32_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv32_activation (LeakyR (None, 56, 56, 128)  0           layer_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 112, 112, 128 0           right_conv32_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 112, 112, 192 0           up_sampling2d_4[0][0]            \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "right_conv21_conv (Conv2D)      (None, 112, 112, 64) 110656      concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_9 (LayerNor (None, 112, 112, 64) 128         right_conv21_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv21_activation (LeakyR (None, 112, 112, 64) 0           layer_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "right_conv22_conv (Conv2D)      (None, 112, 112, 64) 36928       right_conv21_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_10 (LayerNo (None, 112, 112, 64) 128         right_conv22_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv22_activation (LeakyR (None, 112, 112, 64) 0           layer_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 224, 224, 64) 1792        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2D)  (None, 224, 224, 64) 0           right_conv22_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 224, 224, 64) 36928       block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 224, 224, 128 0           up_sampling2d_5[0][0]            \n",
      "                                                                 block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "right_conv11_conv (Conv2D)      (None, 224, 224, 32) 36896       concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_11 (LayerNo (None, 224, 224, 32) 64          right_conv11_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv11_activation (LeakyR (None, 224, 224, 32) 0           layer_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "right_conv12_conv (Conv2D)      (None, 224, 224, 32) 9248        right_conv11_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_12 (LayerNo (None, 224, 224, 32) 64          right_conv12_conv[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "right_conv12_activation (LeakyR (None, 224, 224, 32) 0           layer_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout2d_1 (SpatialDro (None, 224, 224, 32) 0           right_conv12_activation[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 224, 224, 1)  33          spatial_dropout2d_1[0][0]        \n",
      "==================================================================================================\n",
      "Total params: 67,626,529\n",
      "Trainable params: 67,573,409\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(os.path.join(output_dir, 'transfer_learning_iter_100'.format(iterations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=1e-5, decay = 0.1), loss=jaccard_coef_loss, metrics=[jaccard_coef])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 101/200\n",
      "300/300 [==============================] - 539s 2s/step - loss: -0.7290 - jaccard_coef: 0.7290 - val_loss: -0.7260 - val_jaccard_coef: 0.7260\n",
      "\n",
      "Epoch 00101: val_jaccard_coef improved from 0.70109 to 0.72602, saving model to unet/20190526/unet_lesion_20190526_101-0.72602.hdf5\n",
      "Epoch 102/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.7532 - jaccard_coef: 0.7532 - val_loss: -0.7132 - val_jaccard_coef: 0.7132\n",
      "\n",
      "Epoch 00102: val_jaccard_coef did not improve from 0.72602\n",
      "Epoch 103/200\n",
      "300/300 [==============================] - 505s 2s/step - loss: -0.7785 - jaccard_coef: 0.7785 - val_loss: -0.7453 - val_jaccard_coef: 0.7453\n",
      "\n",
      "Epoch 00103: val_jaccard_coef improved from 0.72602 to 0.74528, saving model to unet/20190526/unet_lesion_20190526_103-0.74528.hdf5\n",
      "Epoch 104/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.7880 - jaccard_coef: 0.7880 - val_loss: -0.7564 - val_jaccard_coef: 0.7564\n",
      "\n",
      "Epoch 00104: val_jaccard_coef improved from 0.74528 to 0.75643, saving model to unet/20190526/unet_lesion_20190526_104-0.75643.hdf5\n",
      "Epoch 105/200\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.8034 - jaccard_coef: 0.8034 - val_loss: -0.7368 - val_jaccard_coef: 0.7368\n",
      "\n",
      "Epoch 00105: val_jaccard_coef did not improve from 0.75643\n",
      "Epoch 106/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.8139 - jaccard_coef: 0.8139 - val_loss: -0.7683 - val_jaccard_coef: 0.7683\n",
      "\n",
      "Epoch 00106: val_jaccard_coef improved from 0.75643 to 0.76835, saving model to unet/20190526/unet_lesion_20190526_106-0.76835.hdf5\n",
      "Epoch 107/200\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.8217 - jaccard_coef: 0.8217 - val_loss: -0.7701 - val_jaccard_coef: 0.7701\n",
      "\n",
      "Epoch 00107: val_jaccard_coef improved from 0.76835 to 0.77006, saving model to unet/20190526/unet_lesion_20190526_107-0.77006.hdf5\n",
      "Epoch 108/200\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.8346 - jaccard_coef: 0.8346 - val_loss: -0.7732 - val_jaccard_coef: 0.7732\n",
      "\n",
      "Epoch 00108: val_jaccard_coef improved from 0.77006 to 0.77320, saving model to unet/20190526/unet_lesion_20190526_108-0.77320.hdf5\n",
      "Epoch 109/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.8389 - jaccard_coef: 0.8389 - val_loss: -0.7796 - val_jaccard_coef: 0.7796\n",
      "\n",
      "Epoch 00109: val_jaccard_coef improved from 0.77320 to 0.77961, saving model to unet/20190526/unet_lesion_20190526_109-0.77961.hdf5\n",
      "Epoch 110/200\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.8469 - jaccard_coef: 0.8469 - val_loss: -0.7786 - val_jaccard_coef: 0.7786\n",
      "\n",
      "Epoch 00110: val_jaccard_coef did not improve from 0.77961\n",
      "Epoch 111/200\n",
      "300/300 [==============================] - 505s 2s/step - loss: -0.8503 - jaccard_coef: 0.8503 - val_loss: -0.7872 - val_jaccard_coef: 0.7872\n",
      "\n",
      "Epoch 00111: val_jaccard_coef improved from 0.77961 to 0.78715, saving model to unet/20190526/unet_lesion_20190526_111-0.78715.hdf5\n",
      "Epoch 112/200\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.8594 - jaccard_coef: 0.8594 - val_loss: -0.7796 - val_jaccard_coef: 0.7796\n",
      "\n",
      "Epoch 00112: val_jaccard_coef did not improve from 0.78715\n",
      "Epoch 113/200\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.8596 - jaccard_coef: 0.8596 - val_loss: -0.7775 - val_jaccard_coef: 0.7775\n",
      "\n",
      "Epoch 00113: val_jaccard_coef did not improve from 0.78715\n",
      "Epoch 114/200\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.8633 - jaccard_coef: 0.8633 - val_loss: -0.7891 - val_jaccard_coef: 0.7891\n",
      "\n",
      "Epoch 00114: val_jaccard_coef improved from 0.78715 to 0.78913, saving model to unet/20190526/unet_lesion_20190526_114-0.78913.hdf5\n",
      "Epoch 115/200\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.8668 - jaccard_coef: 0.8668 - val_loss: -0.7896 - val_jaccard_coef: 0.7896\n",
      "\n",
      "Epoch 00115: val_jaccard_coef improved from 0.78913 to 0.78963, saving model to unet/20190526/unet_lesion_20190526_115-0.78963.hdf5\n",
      "Epoch 116/200\n",
      "300/300 [==============================] - 522s 2s/step - loss: -0.8767 - jaccard_coef: 0.8767 - val_loss: -0.7930 - val_jaccard_coef: 0.7930\n",
      "\n",
      "Epoch 00116: val_jaccard_coef improved from 0.78963 to 0.79296, saving model to unet/20190526/unet_lesion_20190526_116-0.79296.hdf5\n",
      "Epoch 117/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.8843 - jaccard_coef: 0.8843 - val_loss: -0.7982 - val_jaccard_coef: 0.7982\n",
      "\n",
      "Epoch 00117: val_jaccard_coef improved from 0.79296 to 0.79822, saving model to unet/20190526/unet_lesion_20190526_117-0.79822.hdf5\n",
      "Epoch 118/200\n",
      "300/300 [==============================] - 521s 2s/step - loss: -0.8822 - jaccard_coef: 0.8822 - val_loss: -0.7892 - val_jaccard_coef: 0.7892\n",
      "\n",
      "Epoch 00118: val_jaccard_coef did not improve from 0.79822\n",
      "Epoch 119/200\n",
      "300/300 [==============================] - 536s 2s/step - loss: -0.8895 - jaccard_coef: 0.8895 - val_loss: -0.8020 - val_jaccard_coef: 0.8020\n",
      "\n",
      "Epoch 00119: val_jaccard_coef improved from 0.79822 to 0.80197, saving model to unet/20190526/unet_lesion_20190526_119-0.80197.hdf5\n",
      "Epoch 120/200\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.8874 - jaccard_coef: 0.8874 - val_loss: -0.7958 - val_jaccard_coef: 0.7958\n",
      "\n",
      "Epoch 00120: val_jaccard_coef did not improve from 0.80197\n",
      "Epoch 121/200\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.8898 - jaccard_coef: 0.8898 - val_loss: -0.8006 - val_jaccard_coef: 0.8006\n",
      "\n",
      "Epoch 00121: val_jaccard_coef did not improve from 0.80197\n",
      "Epoch 122/200\n",
      "300/300 [==============================] - 523s 2s/step - loss: -0.8943 - jaccard_coef: 0.8943 - val_loss: -0.7925 - val_jaccard_coef: 0.7925\n",
      "\n",
      "Epoch 00122: val_jaccard_coef did not improve from 0.80197\n",
      "Epoch 123/200\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.8982 - jaccard_coef: 0.8982 - val_loss: -0.8015 - val_jaccard_coef: 0.8015\n",
      "\n",
      "Epoch 00123: val_jaccard_coef did not improve from 0.80197\n",
      "Epoch 124/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.8970 - jaccard_coef: 0.8970 - val_loss: -0.8004 - val_jaccard_coef: 0.8004\n",
      "\n",
      "Epoch 00124: val_jaccard_coef did not improve from 0.80197\n",
      "Epoch 125/200\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.9047 - jaccard_coef: 0.9047 - val_loss: -0.8052 - val_jaccard_coef: 0.8052\n",
      "\n",
      "Epoch 00125: val_jaccard_coef improved from 0.80197 to 0.80517, saving model to unet/20190526/unet_lesion_20190526_125-0.80517.hdf5\n",
      "Epoch 126/200\n",
      "300/300 [==============================] - 529s 2s/step - loss: -0.9100 - jaccard_coef: 0.9100 - val_loss: -0.7997 - val_jaccard_coef: 0.7997\n",
      "\n",
      "Epoch 00126: val_jaccard_coef did not improve from 0.80517\n",
      "Epoch 127/200\n",
      "300/300 [==============================] - 501s 2s/step - loss: -0.9093 - jaccard_coef: 0.9093 - val_loss: -0.8127 - val_jaccard_coef: 0.8127\n",
      "\n",
      "Epoch 00127: val_jaccard_coef improved from 0.80517 to 0.81265, saving model to unet/20190526/unet_lesion_20190526_127-0.81265.hdf5\n",
      "Epoch 128/200\n",
      "300/300 [==============================] - 521s 2s/step - loss: -0.9118 - jaccard_coef: 0.9118 - val_loss: -0.8004 - val_jaccard_coef: 0.8004\n",
      "\n",
      "Epoch 00128: val_jaccard_coef did not improve from 0.81265\n",
      "Epoch 129/200\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.9141 - jaccard_coef: 0.9141 - val_loss: -0.8066 - val_jaccard_coef: 0.8066\n",
      "\n",
      "Epoch 00129: val_jaccard_coef did not improve from 0.81265\n",
      "Epoch 130/200\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.9118 - jaccard_coef: 0.9118 - val_loss: -0.7963 - val_jaccard_coef: 0.7963\n",
      "\n",
      "Epoch 00130: val_jaccard_coef did not improve from 0.81265\n",
      "Epoch 131/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.9166 - jaccard_coef: 0.9166 - val_loss: -0.8170 - val_jaccard_coef: 0.8170\n",
      "\n",
      "Epoch 00131: val_jaccard_coef improved from 0.81265 to 0.81698, saving model to unet/20190526/unet_lesion_20190526_131-0.81698.hdf5\n",
      "Epoch 132/200\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.9212 - jaccard_coef: 0.9212 - val_loss: -0.8003 - val_jaccard_coef: 0.8003\n",
      "\n",
      "Epoch 00132: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 133/200\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.9230 - jaccard_coef: 0.9230 - val_loss: -0.8081 - val_jaccard_coef: 0.8081\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00133: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 134/200\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.9196 - jaccard_coef: 0.9196 - val_loss: -0.8083 - val_jaccard_coef: 0.8083\n",
      "\n",
      "Epoch 00134: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 135/200\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.9277 - jaccard_coef: 0.9277 - val_loss: -0.8129 - val_jaccard_coef: 0.8129\n",
      "\n",
      "Epoch 00135: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 136/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9266 - jaccard_coef: 0.9266 - val_loss: -0.8145 - val_jaccard_coef: 0.8145\n",
      "\n",
      "Epoch 00136: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 137/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9290 - jaccard_coef: 0.9290 - val_loss: -0.8011 - val_jaccard_coef: 0.8011\n",
      "\n",
      "Epoch 00137: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 138/200\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.9286 - jaccard_coef: 0.9286 - val_loss: -0.8080 - val_jaccard_coef: 0.8080\n",
      "\n",
      "Epoch 00138: val_jaccard_coef did not improve from 0.81698\n",
      "Epoch 139/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9334 - jaccard_coef: 0.9334 - val_loss: -0.8185 - val_jaccard_coef: 0.8185\n",
      "\n",
      "Epoch 00139: val_jaccard_coef improved from 0.81698 to 0.81847, saving model to unet/20190526/unet_lesion_20190526_139-0.81847.hdf5\n",
      "Epoch 140/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9317 - jaccard_coef: 0.9317 - val_loss: -0.8115 - val_jaccard_coef: 0.8115\n",
      "\n",
      "Epoch 00140: val_jaccard_coef did not improve from 0.81847\n",
      "Epoch 141/200\n",
      "300/300 [==============================] - 518s 2s/step - loss: -0.9357 - jaccard_coef: 0.9357 - val_loss: -0.8184 - val_jaccard_coef: 0.8184\n",
      "\n",
      "Epoch 00141: val_jaccard_coef did not improve from 0.81847\n",
      "Epoch 142/200\n",
      "300/300 [==============================] - 503s 2s/step - loss: -0.9372 - jaccard_coef: 0.9372 - val_loss: -0.8041 - val_jaccard_coef: 0.8041\n",
      "\n",
      "Epoch 00142: val_jaccard_coef did not improve from 0.81847\n",
      "Epoch 143/200\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.9314 - jaccard_coef: 0.9314 - val_loss: -0.8195 - val_jaccard_coef: 0.8195\n",
      "\n",
      "Epoch 00143: val_jaccard_coef improved from 0.81847 to 0.81954, saving model to unet/20190526/unet_lesion_20190526_143-0.81954.hdf5\n",
      "Epoch 144/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9387 - jaccard_coef: 0.9387 - val_loss: -0.8157 - val_jaccard_coef: 0.8157\n",
      "\n",
      "Epoch 00144: val_jaccard_coef did not improve from 0.81954\n",
      "Epoch 145/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.9403 - jaccard_coef: 0.9403 - val_loss: -0.8182 - val_jaccard_coef: 0.8182\n",
      "\n",
      "Epoch 00145: val_jaccard_coef did not improve from 0.81954\n",
      "Epoch 146/200\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.9410 - jaccard_coef: 0.9410 - val_loss: -0.8096 - val_jaccard_coef: 0.8096\n",
      "\n",
      "Epoch 00146: val_jaccard_coef did not improve from 0.81954\n",
      "Epoch 147/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9413 - jaccard_coef: 0.9413 - val_loss: -0.8180 - val_jaccard_coef: 0.8180\n",
      "\n",
      "Epoch 00147: val_jaccard_coef did not improve from 0.81954\n",
      "Epoch 148/200\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.9431 - jaccard_coef: 0.9431 - val_loss: -0.8092 - val_jaccard_coef: 0.8092\n",
      "\n",
      "Epoch 00148: val_jaccard_coef did not improve from 0.81954\n",
      "Epoch 149/200\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.9441 - jaccard_coef: 0.9441 - val_loss: -0.8133 - val_jaccard_coef: 0.8133\n",
      "\n",
      "Epoch 00149: val_jaccard_coef did not improve from 0.81954\n",
      "Epoch 150/200\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.9449 - jaccard_coef: 0.9449 - val_loss: -0.8235 - val_jaccard_coef: 0.8235\n",
      "\n",
      "Epoch 00150: val_jaccard_coef improved from 0.81954 to 0.82347, saving model to unet/20190526/unet_lesion_20190526_150-0.82347.hdf5\n",
      "Epoch 151/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9478 - jaccard_coef: 0.9478 - val_loss: -0.8240 - val_jaccard_coef: 0.8240\n",
      "\n",
      "Epoch 00151: val_jaccard_coef improved from 0.82347 to 0.82403, saving model to unet/20190526/unet_lesion_20190526_151-0.82403.hdf5\n",
      "Epoch 152/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.9454 - jaccard_coef: 0.9454 - val_loss: -0.8096 - val_jaccard_coef: 0.8096\n",
      "\n",
      "Epoch 00152: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 153/200\n",
      "300/300 [==============================] - 508s 2s/step - loss: -0.9470 - jaccard_coef: 0.9470 - val_loss: -0.8180 - val_jaccard_coef: 0.8180\n",
      "\n",
      "Epoch 00153: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 154/200\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.9473 - jaccard_coef: 0.9473 - val_loss: -0.8116 - val_jaccard_coef: 0.8116\n",
      "\n",
      "Epoch 00154: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 155/200\n",
      "300/300 [==============================] - 503s 2s/step - loss: -0.9479 - jaccard_coef: 0.9479 - val_loss: -0.8130 - val_jaccard_coef: 0.8130\n",
      "\n",
      "Epoch 00155: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 156/200\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.9495 - jaccard_coef: 0.9495 - val_loss: -0.8139 - val_jaccard_coef: 0.8139\n",
      "\n",
      "Epoch 00156: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 157/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9502 - jaccard_coef: 0.9502 - val_loss: -0.8047 - val_jaccard_coef: 0.8047\n",
      "\n",
      "Epoch 00157: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 158/200\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.9515 - jaccard_coef: 0.9515 - val_loss: -0.8228 - val_jaccard_coef: 0.8228\n",
      "\n",
      "Epoch 00158: val_jaccard_coef did not improve from 0.82403\n",
      "Epoch 159/200\n",
      "300/300 [==============================] - 504s 2s/step - loss: -0.9539 - jaccard_coef: 0.9539 - val_loss: -0.8262 - val_jaccard_coef: 0.8262\n",
      "\n",
      "Epoch 00159: val_jaccard_coef improved from 0.82403 to 0.82616, saving model to unet/20190526/unet_lesion_20190526_159-0.82616.hdf5\n",
      "Epoch 160/200\n",
      "300/300 [==============================] - 525s 2s/step - loss: -0.9551 - jaccard_coef: 0.9551 - val_loss: -0.8191 - val_jaccard_coef: 0.8191\n",
      "\n",
      "Epoch 00160: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 161/200\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.9511 - jaccard_coef: 0.9511 - val_loss: -0.8195 - val_jaccard_coef: 0.8195\n",
      "\n",
      "Epoch 00161: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 162/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.9541 - jaccard_coef: 0.9541 - val_loss: -0.8158 - val_jaccard_coef: 0.8158\n",
      "\n",
      "Epoch 00162: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 163/200\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.9488 - jaccard_coef: 0.9488 - val_loss: -0.8243 - val_jaccard_coef: 0.8243\n",
      "\n",
      "Epoch 00163: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 164/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9538 - jaccard_coef: 0.9538 - val_loss: -0.8240 - val_jaccard_coef: 0.8240\n",
      "\n",
      "Epoch 00164: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 165/200\n",
      "300/300 [==============================] - 501s 2s/step - loss: -0.9585 - jaccard_coef: 0.9585 - val_loss: -0.8202 - val_jaccard_coef: 0.8202\n",
      "\n",
      "Epoch 00165: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 166/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9578 - jaccard_coef: 0.9578 - val_loss: -0.8169 - val_jaccard_coef: 0.8169\n",
      "\n",
      "Epoch 00166: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 167/200\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.9553 - jaccard_coef: 0.9553 - val_loss: -0.8204 - val_jaccard_coef: 0.8204\n",
      "\n",
      "Epoch 00167: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 168/200\n",
      "300/300 [==============================] - 505s 2s/step - loss: -0.9587 - jaccard_coef: 0.9587 - val_loss: -0.8225 - val_jaccard_coef: 0.8225\n",
      "\n",
      "Epoch 00168: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 169/200\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.9566 - jaccard_coef: 0.9566 - val_loss: -0.8091 - val_jaccard_coef: 0.8091\n",
      "\n",
      "Epoch 00169: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 170/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300/300 [==============================] - 507s 2s/step - loss: -0.9568 - jaccard_coef: 0.9568 - val_loss: -0.8213 - val_jaccard_coef: 0.8213\n",
      "\n",
      "Epoch 00170: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 171/200\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.9598 - jaccard_coef: 0.9598 - val_loss: -0.8210 - val_jaccard_coef: 0.8210\n",
      "\n",
      "Epoch 00171: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 172/200\n",
      "300/300 [==============================] - 505s 2s/step - loss: -0.9565 - jaccard_coef: 0.9565 - val_loss: -0.8065 - val_jaccard_coef: 0.8065\n",
      "\n",
      "Epoch 00172: val_jaccard_coef did not improve from 0.82616\n",
      "Epoch 173/200\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.9611 - jaccard_coef: 0.9611 - val_loss: -0.8281 - val_jaccard_coef: 0.8281\n",
      "\n",
      "Epoch 00173: val_jaccard_coef improved from 0.82616 to 0.82810, saving model to unet/20190526/unet_lesion_20190526_173-0.82810.hdf5\n",
      "Epoch 174/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.9606 - jaccard_coef: 0.9606 - val_loss: -0.8221 - val_jaccard_coef: 0.8221\n",
      "\n",
      "Epoch 00174: val_jaccard_coef did not improve from 0.82810\n",
      "Epoch 175/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9608 - jaccard_coef: 0.9608 - val_loss: -0.8315 - val_jaccard_coef: 0.8315\n",
      "\n",
      "Epoch 00175: val_jaccard_coef improved from 0.82810 to 0.83154, saving model to unet/20190526/unet_lesion_20190526_175-0.83154.hdf5\n",
      "Epoch 176/200\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.9616 - jaccard_coef: 0.9616 - val_loss: -0.8214 - val_jaccard_coef: 0.8214\n",
      "\n",
      "Epoch 00176: val_jaccard_coef did not improve from 0.83154\n",
      "Epoch 177/200\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.9616 - jaccard_coef: 0.9616 - val_loss: -0.8135 - val_jaccard_coef: 0.8135\n",
      "\n",
      "Epoch 00177: val_jaccard_coef did not improve from 0.83154\n",
      "Epoch 178/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9645 - jaccard_coef: 0.9645 - val_loss: -0.8199 - val_jaccard_coef: 0.8199\n",
      "\n",
      "Epoch 00178: val_jaccard_coef did not improve from 0.83154\n",
      "Epoch 179/200\n",
      "300/300 [==============================] - 495s 2s/step - loss: -0.9621 - jaccard_coef: 0.9621 - val_loss: -0.8219 - val_jaccard_coef: 0.8219\n",
      "\n",
      "Epoch 00179: val_jaccard_coef did not improve from 0.83154\n",
      "Epoch 180/200\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.9626 - jaccard_coef: 0.9626 - val_loss: -0.8320 - val_jaccard_coef: 0.8320\n",
      "\n",
      "Epoch 00180: val_jaccard_coef improved from 0.83154 to 0.83202, saving model to unet/20190526/unet_lesion_20190526_180-0.83202.hdf5\n",
      "Epoch 181/200\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.9622 - jaccard_coef: 0.9622 - val_loss: -0.8128 - val_jaccard_coef: 0.8128\n",
      "\n",
      "Epoch 00181: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 182/200\n",
      "300/300 [==============================] - 517s 2s/step - loss: -0.9642 - jaccard_coef: 0.9642 - val_loss: -0.8270 - val_jaccard_coef: 0.8270\n",
      "\n",
      "Epoch 00182: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 183/200\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.9649 - jaccard_coef: 0.9649 - val_loss: -0.8259 - val_jaccard_coef: 0.8259\n",
      "\n",
      "Epoch 00183: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 184/200\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.9651 - jaccard_coef: 0.9651 - val_loss: -0.8219 - val_jaccard_coef: 0.8219\n",
      "\n",
      "Epoch 00184: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 185/200\n",
      "300/300 [==============================] - 519s 2s/step - loss: -0.9636 - jaccard_coef: 0.9636 - val_loss: -0.8237 - val_jaccard_coef: 0.8237\n",
      "\n",
      "Epoch 00185: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 186/200\n",
      "300/300 [==============================] - 506s 2s/step - loss: -0.9645 - jaccard_coef: 0.9645 - val_loss: -0.8221 - val_jaccard_coef: 0.8221\n",
      "\n",
      "Epoch 00186: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 187/200\n",
      "300/300 [==============================] - 515s 2s/step - loss: -0.9666 - jaccard_coef: 0.9666 - val_loss: -0.8233 - val_jaccard_coef: 0.8233\n",
      "\n",
      "Epoch 00187: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 188/200\n",
      "300/300 [==============================] - 509s 2s/step - loss: -0.9646 - jaccard_coef: 0.9646 - val_loss: -0.8221 - val_jaccard_coef: 0.8221\n",
      "\n",
      "Epoch 00188: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 189/200\n",
      "300/300 [==============================] - 514s 2s/step - loss: -0.9668 - jaccard_coef: 0.9668 - val_loss: -0.8160 - val_jaccard_coef: 0.8160\n",
      "\n",
      "Epoch 00189: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 190/200\n",
      "300/300 [==============================] - 507s 2s/step - loss: -0.9661 - jaccard_coef: 0.9661 - val_loss: -0.8231 - val_jaccard_coef: 0.8231\n",
      "\n",
      "Epoch 00190: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 191/200\n",
      "300/300 [==============================] - 513s 2s/step - loss: -0.9673 - jaccard_coef: 0.9673 - val_loss: -0.8210 - val_jaccard_coef: 0.8210\n",
      "\n",
      "Epoch 00191: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 192/200\n",
      "300/300 [==============================] - 521s 2s/step - loss: -0.9670 - jaccard_coef: 0.9670 - val_loss: -0.8277 - val_jaccard_coef: 0.8277\n",
      "\n",
      "Epoch 00192: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 193/200\n",
      "300/300 [==============================] - 510s 2s/step - loss: -0.9676 - jaccard_coef: 0.9676 - val_loss: -0.8198 - val_jaccard_coef: 0.8198\n",
      "\n",
      "Epoch 00193: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 194/200\n",
      "300/300 [==============================] - 508s 2s/step - loss: -0.9641 - jaccard_coef: 0.9641 - val_loss: -0.8222 - val_jaccard_coef: 0.8222\n",
      "\n",
      "Epoch 00194: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 195/200\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.9666 - jaccard_coef: 0.9666 - val_loss: -0.8081 - val_jaccard_coef: 0.8081\n",
      "\n",
      "Epoch 00195: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 196/200\n",
      "300/300 [==============================] - 511s 2s/step - loss: -0.9677 - jaccard_coef: 0.9677 - val_loss: -0.8194 - val_jaccard_coef: 0.8194\n",
      "\n",
      "Epoch 00196: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 197/200\n",
      "300/300 [==============================] - 502s 2s/step - loss: -0.9681 - jaccard_coef: 0.9681 - val_loss: -0.8161 - val_jaccard_coef: 0.8161\n",
      "\n",
      "Epoch 00197: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 198/200\n",
      "300/300 [==============================] - 512s 2s/step - loss: -0.9667 - jaccard_coef: 0.9667 - val_loss: -0.8184 - val_jaccard_coef: 0.8184\n",
      "\n",
      "Epoch 00198: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 199/200\n",
      "300/300 [==============================] - 516s 2s/step - loss: -0.9701 - jaccard_coef: 0.9701 - val_loss: -0.8186 - val_jaccard_coef: 0.8186\n",
      "\n",
      "Epoch 00199: val_jaccard_coef did not improve from 0.83202\n",
      "Epoch 200/200\n",
      "300/300 [==============================] - 508s 2s/step - loss: -0.9696 - jaccard_coef: 0.9696 - val_loss: -0.8247 - val_jaccard_coef: 0.8247\n",
      "\n",
      "Epoch 00200: val_jaccard_coef did not improve from 0.83202\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=Adam(lr=1e-5), loss=jaccard_coef_loss, metrics=[jaccard_coef])\n",
    "history2 = model.fit_generator(\n",
    "    myGene,\n",
    "    steps_per_epoch = 300, \n",
    "    epochs=200,\n",
    "    initial_epoch = iterations,\n",
    "    callbacks=[model_checkpoint],\n",
    "    validation_data=myValGene,\n",
    "    validation_steps=100)\n",
    "\n",
    "model.save(os.path.join(output_dir, 'transfer_learning_iter_200'.format(iterations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISIC_0000028.jpg -- jaccard index: 0.9637197852134705\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_names = next(os.walk(test_data_dir))[2]\n",
    "\n",
    "showPredictResult(file_names[3], model, 224, 224)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training co-effiency    : 0.7184177907064433;\n",
      "Validation co-effiency : 0.6917850740590886\n"
     ]
    }
   ],
   "source": [
    "# Plot Training curve\n",
    "plotTrainigGraph(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/test/images/ISIC_0000006.jpg -- jaccard index: 0.8364076018333435\n",
      "(224, 224, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file = 'data/test/images/ISIC_0000006.jpg'\n",
    "#grey_img = load_img(os.path.join(test_data_dir,file), target_size=(img_rows, img_cols), grayscale=False)\n",
    "#mask_img = load_img(os.path.join(test_data_mask_dir,file.split('.')[0]+\"_segmentation.png\"), target_size=(224, 224), grayscale=True)\n",
    "grey_img = load_img(file, target_size=(224, 224), grayscale=False)\n",
    "mask_img = load_img('data/test/masks/ISIC_0000006_segmentation.png', target_size=(224, 224), grayscale=True)\n",
    "\n",
    "img = img_to_array(grey_img)\n",
    "img_mask = img_to_array(mask_img)\n",
    "\n",
    "img, img_mask = normalizeData(img, img_mask)\n",
    "img = np.reshape(img,(1,)+img.shape)\n",
    "\n",
    "pred = model.predict([img])\n",
    "sess = tf.Session()\n",
    "score = sess.run(jaccard_coef(img_mask, pred))\n",
    "print(\"{} -- jaccard index: {}\".format(file,score))\n",
    "\n",
    "result_img = array_to_img(pred[0] * 255 )\n",
    "print(pred[0].shape)\n",
    "f, ax = plt.subplots(1,2, figsize = (50,50))\n",
    "ax[0].imshow(grey_img) \n",
    "ax[0].axis('off')\n",
    "ax[0].set_title('image')\n",
    "ax[1].imshow(result_img)\n",
    "ax[1].axis('off')\n",
    "ax[1].set_title('mask')\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training co-effiency    : 0.9701533100399135;\n",
      "Validation co-effiency : 0.818605121356159\n"
     ]
    }
   ],
   "source": [
    "coef = np.array(hist['jaccard_coef'])\n",
    "val_coef = np.array(hist['val_jaccard_coef'])\n",
    "print(\"Training co-effiency    : {};\\nValidation co-effiency : {}\".format(coef[coef==max(coef)][0], val_coef[np.argmax(coef)]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictTestSet(model, img_rows, img_cols):\n",
    "    file_names = next(os.walk(test_data_dir))[2]\n",
    "    scores = []\n",
    "    for file in file_names:\n",
    "        grey_img = load_img(os.path.join(test_data_dir,file), target_size=(img_rows, img_cols), grayscale=False)\n",
    "        mask_img = load_img(os.path.join(test_data_mask_dir,file.split('.')[0]+\"_segmentation.png\"), \n",
    "                            target_size=(img_rows, img_cols), grayscale=True)\n",
    "        img = img_to_array(grey_img)\n",
    "        img_mask = img_to_array(mask_img)\n",
    "\n",
    "        img, img_mask = normalizeData(img, img_mask)\n",
    "        img = np.reshape(img,(1,)+img.shape)\n",
    "\n",
    "        pred = model.predict([img])\n",
    "        sess = tf.Session()\n",
    "        score = sess.run(jaccard_coef(img_mask, pred))\n",
    "        print(\"{} -- jaccard index: {}\".format(file,score))\n",
    "        scores.append([file,score])\n",
    "\n",
    "        result_img = array_to_img(pred[0] * 255 )\n",
    "        result_img.save(os.path.join(test_data_pred_dir, file.split('.')[0] + '_predict.jpg'))\n",
    "\n",
    "    with open(\"unet_test_result.csv\", 'w') as f:\n",
    "        f.write(\"filename, jaccard_index\\n\")\n",
    "        for i in range(len(scores)):\n",
    "        #print(scores[i])\n",
    "            f.write(\"{},{}\\n\".format(scores[i][0], scores[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISIC_0000006.jpg -- jaccard index: 0.805846631526947\n",
      "ISIC_0000012.jpg -- jaccard index: 0.9086361527442932\n",
      "ISIC_0000020.jpg -- jaccard index: 0.9457604885101318\n",
      "ISIC_0000028.jpg -- jaccard index: 0.9664589762687683\n",
      "ISIC_0000029.jpg -- jaccard index: 0.9081190228462219\n",
      "ISIC_0000032.jpg -- jaccard index: 0.7644633054733276\n",
      "ISIC_0000034.jpg -- jaccard index: 0.9640210866928101\n",
      "ISIC_0000036.jpg -- jaccard index: 0.9463136196136475\n",
      "ISIC_0000046.jpg -- jaccard index: 0.8761652708053589\n",
      "ISIC_0000047.jpg -- jaccard index: 0.8867477774620056\n",
      "ISIC_0000048.jpg -- jaccard index: 0.9185196161270142\n",
      "ISIC_0000049.jpg -- jaccard index: 0.844952404499054\n",
      "ISIC_0000060.jpg -- jaccard index: 0.9158393144607544\n",
      "ISIC_0000066.jpg -- jaccard index: 0.9135443568229675\n",
      "ISIC_0000075.jpg -- jaccard index: 0.954032301902771\n",
      "ISIC_0000081.jpg -- jaccard index: 0.5965604186058044\n",
      "ISIC_0000085.jpg -- jaccard index: 0.9234873652458191\n",
      "ISIC_0000087.jpg -- jaccard index: 0.9424974322319031\n",
      "ISIC_0000089.jpg -- jaccard index: 0.8962939977645874\n",
      "ISIC_0000092.jpg -- jaccard index: 0.8531364798545837\n",
      "ISIC_0000095.jpg -- jaccard index: 0.8058618307113647\n",
      "ISIC_0000112.jpg -- jaccard index: 0.9002153277397156\n",
      "ISIC_0000119.jpg -- jaccard index: 0.9005452394485474\n",
      "ISIC_0000125.jpg -- jaccard index: 0.7536256909370422\n",
      "ISIC_0000135.jpg -- jaccard index: 0.9648571610450745\n",
      "ISIC_0000137.jpg -- jaccard index: 0.9582486152648926\n",
      "ISIC_0000142.jpg -- jaccard index: 0.9300578236579895\n",
      "ISIC_0000149.jpg -- jaccard index: 0.8590757846832275\n",
      "ISIC_0000155.jpg -- jaccard index: 0.9777218699455261\n",
      "ISIC_0000157.jpg -- jaccard index: 0.9412361979484558\n",
      "ISIC_0000160.jpg -- jaccard index: 0.9655576944351196\n",
      "ISIC_0000162.jpg -- jaccard index: 0.9565619826316833\n",
      "ISIC_0000163.jpg -- jaccard index: 0.866376519203186\n",
      "ISIC_0000167.jpg -- jaccard index: 0.8611896634101868\n",
      "ISIC_0000170.jpg -- jaccard index: 0.8468679189682007\n",
      "ISIC_0000173.jpg -- jaccard index: 0.8863722681999207\n",
      "ISIC_0000179.jpg -- jaccard index: 0.7948341369628906\n",
      "ISIC_0000190.jpg -- jaccard index: 0.9456053972244263\n",
      "ISIC_0000200.jpg -- jaccard index: 0.7566563487052917\n",
      "ISIC_0000202.jpg -- jaccard index: 0.8616384267807007\n",
      "ISIC_0000203.jpg -- jaccard index: 0.9781466126441956\n",
      "ISIC_0000204.jpg -- jaccard index: 0.9471024870872498\n",
      "ISIC_0000212.jpg -- jaccard index: 0.901674211025238\n",
      "ISIC_0000215.jpg -- jaccard index: 0.9017515778541565\n",
      "ISIC_0000216.jpg -- jaccard index: 0.9776790738105774\n",
      "ISIC_0000219.jpg -- jaccard index: 0.838664710521698\n",
      "ISIC_0000221.jpg -- jaccard index: 0.9034408330917358\n",
      "ISIC_0000226.jpg -- jaccard index: 0.8134028911590576\n",
      "ISIC_0000228.jpg -- jaccard index: 0.9592288136482239\n",
      "ISIC_0000243.jpg -- jaccard index: 0.9364264011383057\n",
      "ISIC_0000246.jpg -- jaccard index: 0.884879469871521\n",
      "ISIC_0000247.jpg -- jaccard index: 0.8170192837715149\n",
      "ISIC_0000249.jpg -- jaccard index: 0.8893096446990967\n",
      "ISIC_0000252.jpg -- jaccard index: 0.932510495185852\n",
      "ISIC_0000260.jpg -- jaccard index: 0.8436068296432495\n",
      "ISIC_0000268.jpg -- jaccard index: 0.9220763444900513\n",
      "ISIC_0000297.jpg -- jaccard index: 0.9309911131858826\n",
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      "ISIC_0013321.jpg -- jaccard index: 0.12344592809677124\n",
      "ISIC_0013325.jpg -- jaccard index: 0.8720219135284424\n",
      "ISIC_0013369.jpg -- jaccard index: 0.8653120398521423\n",
      "ISIC_0013390.jpg -- jaccard index: 0.8899648785591125\n",
      "ISIC_0013403.jpg -- jaccard index: 0.9290749430656433\n",
      "ISIC_0013414.jpg -- jaccard index: 0.6033511757850647\n",
      "ISIC_0013428.jpg -- jaccard index: 0.8839846849441528\n",
      "ISIC_0013458.jpg -- jaccard index: 0.9153699278831482\n",
      "ISIC_0013459.jpg -- jaccard index: 0.8479030728340149\n",
      "ISIC_0013565.jpg -- jaccard index: 0.07495307922363281\n",
      "ISIC_0013580.jpg -- jaccard index: 0.8782708644866943\n",
      "ISIC_0013581.jpg -- jaccard index: 0.7796856760978699\n",
      "ISIC_0013603.jpg -- jaccard index: 0.8482694029808044\n",
      "ISIC_0013610.jpg -- jaccard index: 0.8775798678398132\n",
      "ISIC_0013615.jpg -- jaccard index: 0.8035046458244324\n",
      "ISIC_0013672.jpg -- jaccard index: 0.8928216695785522\n",
      "ISIC_0013678.jpg -- jaccard index: 0.9072281122207642\n",
      "ISIC_0013690.jpg -- jaccard index: 0.528321385383606\n",
      "ISIC_0013695.jpg -- jaccard index: 0.7369675636291504\n",
      "ISIC_0013702.jpg -- jaccard index: 0.8203230500221252\n",
      "ISIC_0013712.jpg -- jaccard index: 0.7564918398857117\n",
      "ISIC_0013713.jpg -- jaccard index: 0.87435382604599\n",
      "ISIC_0013721.jpg -- jaccard index: 0.7815557718276978\n",
      "ISIC_0013738.jpg -- jaccard index: 0.8834306597709656\n",
      "ISIC_0013793.jpg -- jaccard index: 0.8772413730621338\n",
      "ISIC_0013794.jpg -- jaccard index: 0.910680890083313\n",
      "ISIC_0013803.jpg -- jaccard index: 0.8082835674285889\n",
      "ISIC_0013807.jpg -- jaccard index: 0.7386781573295593\n",
      "ISIC_0013815.jpg -- jaccard index: 0.7975717782974243\n",
      "ISIC_0013832.jpg -- jaccard index: 0.6986171007156372\n",
      "ISIC_0013839.jpg -- jaccard index: 0.719124436378479\n",
      "ISIC_0013863.jpg -- jaccard index: 0.004767168313264847\n",
      "ISIC_0013867.jpg -- jaccard index: 0.812686562538147\n",
      "ISIC_0013897.jpg -- jaccard index: 0.8412742614746094\n",
      "ISIC_0013929.jpg -- jaccard index: 0.3883441984653473\n",
      "ISIC_0013942.jpg -- jaccard index: 0.4174391031265259\n",
      "ISIC_0013962.jpg -- jaccard index: 0.848546028137207\n",
      "ISIC_0013966.jpg -- jaccard index: 0.12047700583934784\n",
      "ISIC_0013970.jpg -- jaccard index: 0.395235151052475\n",
      "ISIC_0013972.jpg -- jaccard index: 0.49038195610046387\n",
      "ISIC_0013996.jpg -- jaccard index: 0.8469336628913879\n",
      "ISIC_0014026.jpg -- jaccard index: 0.8901680111885071\n",
      "ISIC_0014028.jpg -- jaccard index: 0.8983648419380188\n",
      "ISIC_0014073.jpg -- jaccard index: 0.8532041311264038\n",
      "ISIC_0014076.jpg -- jaccard index: 0.8494164943695068\n",
      "ISIC_0014089.jpg -- jaccard index: 0.824396550655365\n",
      "ISIC_0014092.jpg -- jaccard index: 0.834162175655365\n",
      "ISIC_0014110.jpg -- jaccard index: 0.943304717540741\n",
      "ISIC_0014136.jpg -- jaccard index: 0.7935012578964233\n",
      "ISIC_0014150.jpg -- jaccard index: 0.9427428245544434\n",
      "ISIC_0014158.jpg -- jaccard index: 0.9487789273262024\n",
      "ISIC_0014173.jpg -- jaccard index: 0.6726108193397522\n",
      "ISIC_0014183.jpg -- jaccard index: 0.5441625118255615\n",
      "ISIC_0014190.jpg -- jaccard index: 0.7467182278633118\n",
      "ISIC_0014248.jpg -- jaccard index: 0.6081129908561707\n",
      "ISIC_0014253.jpg -- jaccard index: 0.9001789689064026\n",
      "ISIC_0014273.jpg -- jaccard index: 0.8934988379478455\n",
      "ISIC_0014311.jpg -- jaccard index: 0.888369619846344\n",
      "ISIC_0014316.jpg -- jaccard index: 0.8768293261528015\n",
      "ISIC_0014336.jpg -- jaccard index: 0.809524416923523\n",
      "ISIC_0014361.jpg -- jaccard index: 0.8645220398902893\n",
      "ISIC_0014394.jpg -- jaccard index: 0.7296637892723083\n",
      "ISIC_0014410.jpg -- jaccard index: 0.7683040499687195\n",
      "ISIC_0014419.jpg -- jaccard index: 0.533645510673523\n",
      "ISIC_0014434.jpg -- jaccard index: 0.760291576385498\n",
      "ISIC_0014458.jpg -- jaccard index: 0.8898914456367493\n",
      "ISIC_0014476.jpg -- jaccard index: 0.5949077606201172\n",
      "ISIC_0014486.jpg -- jaccard index: 0.7080849409103394\n",
      "ISIC_0014506.jpg -- jaccard index: 0.8321760892868042\n",
      "ISIC_0014525.jpg -- jaccard index: 0.31141412258148193\n",
      "ISIC_0014526.jpg -- jaccard index: 0.78143709897995\n",
      "ISIC_0014545.jpg -- jaccard index: 0.9349827766418457\n",
      "ISIC_0014546.jpg -- jaccard index: 0.8300022482872009\n",
      "ISIC_0014580.jpg -- jaccard index: 0.7692463994026184\n",
      "ISIC_0014601.jpg -- jaccard index: 0.8441296219825745\n",
      "ISIC_0014603.jpg -- jaccard index: 0.9104424715042114\n",
      "ISIC_0014632.jpg -- jaccard index: 0.5138846039772034\n",
      "ISIC_0014657.jpg -- jaccard index: 0.8989549279212952\n",
      "ISIC_0014665.jpg -- jaccard index: 0.8938654661178589\n",
      "ISIC_0014666.jpg -- jaccard index: 0.7213953137397766\n",
      "ISIC_0014680.jpg -- jaccard index: 0.8538600206375122\n",
      "ISIC_0014688.jpg -- jaccard index: 0.8256704211235046\n",
      "ISIC_0014692.jpg -- jaccard index: 0.8270557522773743\n",
      "ISIC_0014694.jpg -- jaccard index: 0.9476714730262756\n",
      "ISIC_0014708.jpg -- jaccard index: 0.8408657908439636\n",
      "ISIC_0014713.jpg -- jaccard index: 0.7311722636222839\n",
      "ISIC_0014722.jpg -- jaccard index: 0.9393177032470703\n",
      "ISIC_0014723.jpg -- jaccard index: 0.8491374254226685\n",
      "ISIC_0014724.jpg -- jaccard index: 0.9070740938186646\n",
      "ISIC_0014726.jpg -- jaccard index: 0.9154903888702393\n",
      "ISIC_0014735.jpg -- jaccard index: 0.9717779159545898\n",
      "ISIC_0014739.jpg -- jaccard index: 0.8641765713691711\n",
      "ISIC_0014743.jpg -- jaccard index: 0.8753255605697632\n",
      "ISIC_0014745.jpg -- jaccard index: 0.9434250593185425\n",
      "ISIC_0014753.jpg -- jaccard index: 0.9302456378936768\n",
      "ISIC_0014760.jpg -- jaccard index: 0.942820131778717\n",
      "ISIC_0014775.jpg -- jaccard index: 0.6324241757392883\n",
      "ISIC_0014780.jpg -- jaccard index: 0.881938099861145\n",
      "ISIC_0014787.jpg -- jaccard index: 0.8146777749061584\n",
      "ISIC_0014794.jpg -- jaccard index: 0.9334730505943298\n",
      "ISIC_0014805.jpg -- jaccard index: 0.921985387802124\n",
      "ISIC_0014826.jpg -- jaccard index: 0.9027297496795654\n",
      "ISIC_0014838.jpg -- jaccard index: 0.8929662108421326\n",
      "ISIC_0014844.jpg -- jaccard index: 0.8759695291519165\n",
      "ISIC_0014845.jpg -- jaccard index: 0.9312043786048889\n",
      "ISIC_0014846.jpg -- jaccard index: 0.946824312210083\n",
      "ISIC_0014848.jpg -- jaccard index: 0.9349836707115173\n",
      "ISIC_0014855.jpg -- jaccard index: 0.8772407174110413\n",
      "ISIC_0014857.jpg -- jaccard index: 0.907884955406189\n",
      "ISIC_0014930.jpg -- jaccard index: 0.9298645853996277\n",
      "ISIC_0014932.jpg -- jaccard index: 0.5217450857162476\n",
      "ISIC_0014936.jpg -- jaccard index: 0.8889894485473633\n",
      "ISIC_0014947.jpg -- jaccard index: 0.9554582834243774\n",
      "ISIC_0014951.jpg -- jaccard index: 0.9606184959411621\n",
      "ISIC_0014956.jpg -- jaccard index: 0.9019936919212341\n",
      "ISIC_0014968.jpg -- jaccard index: 0.9056032299995422\n",
      "ISIC_0014974.jpg -- jaccard index: 0.9240227341651917\n",
      "ISIC_0014983.jpg -- jaccard index: 0.8753762245178223\n",
      "ISIC_0014985.jpg -- jaccard index: 0.7755744457244873\n",
      "ISIC_0014987.jpg -- jaccard index: 0.9102707505226135\n",
      "ISIC_0015008.jpg -- jaccard index: 0.5223630666732788\n",
      "ISIC_0015016.jpg -- jaccard index: 0.9200805425643921\n",
      "ISIC_0015030.jpg -- jaccard index: 0.8836147785186768\n",
      "ISIC_0015034.jpg -- jaccard index: 0.971416175365448\n",
      "ISIC_0015040.jpg -- jaccard index: 0.6369011998176575\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISIC_0015044.jpg -- jaccard index: 0.8442737460136414\n",
      "ISIC_0015057.jpg -- jaccard index: 0.8100244998931885\n",
      "ISIC_0015108.jpg -- jaccard index: 0.8895128965377808\n",
      "ISIC_0015125.jpg -- jaccard index: 0.9254015684127808\n",
      "ISIC_0015152.jpg -- jaccard index: 0.6910988092422485\n",
      "ISIC_0015153.jpg -- jaccard index: 0.8630827069282532\n",
      "ISIC_0015160.jpg -- jaccard index: 0.9270668625831604\n",
      "ISIC_0015174.jpg -- jaccard index: 0.8674670457839966\n",
      "ISIC_0015190.jpg -- jaccard index: 0.8429864048957825\n",
      "ISIC_0015193.jpg -- jaccard index: 0.9394819736480713\n",
      "ISIC_0015207.jpg -- jaccard index: 0.7111222147941589\n",
      "ISIC_0015208.jpg -- jaccard index: 0.9564371109008789\n",
      "ISIC_0015211.jpg -- jaccard index: 0.654089093208313\n",
      "ISIC_0015212.jpg -- jaccard index: 0.9490787386894226\n",
      "ISIC_0015215.jpg -- jaccard index: 0.8766003847122192\n",
      "ISIC_0015223.jpg -- jaccard index: 0.9108254313468933\n",
      "ISIC_0015229.jpg -- jaccard index: 0.878642201423645\n",
      "ISIC_0015241.jpg -- jaccard index: 0.7558485865592957\n",
      "ISIC_0015279.jpg -- jaccard index: 0.8264461159706116\n",
      "ISIC_0015293.jpg -- jaccard index: 0.8509967923164368\n",
      "ISIC_0015309.jpg -- jaccard index: 0.9390509724617004\n",
      "ISIC_0015312.jpg -- jaccard index: 0.9291189312934875\n",
      "ISIC_0015360.jpg -- jaccard index: 0.7373498678207397\n",
      "ISIC_0015368.jpg -- jaccard index: 0.9592377543449402\n",
      "ISIC_0015412.jpg -- jaccard index: 0.8218285441398621\n",
      "ISIC_0015440.jpg -- jaccard index: 0.8180561065673828\n",
      "ISIC_0015476.jpg -- jaccard index: 0.9235695004463196\n",
      "ISIC_0015483.jpg -- jaccard index: 0.8440869450569153\n",
      "ISIC_0015510.jpg -- jaccard index: 0.8918011784553528\n",
      "ISIC_0015563.jpg -- jaccard index: 0.910915732383728\n",
      "ISIC_0015568.jpg -- jaccard index: 0.8961878418922424\n",
      "ISIC_0015593.jpg -- jaccard index: 0.8644830584526062\n",
      "ISIC_0015607.jpg -- jaccard index: 0.41745904088020325\n",
      "ISIC_0015941.jpg -- jaccard index: 0.8887211680412292\n",
      "ISIC_0015946.jpg -- jaccard index: 0.9239130616188049\n",
      "ISIC_0015948.jpg -- jaccard index: 0.7814426422119141\n",
      "ISIC_0015949.jpg -- jaccard index: 0.927920401096344\n",
      "ISIC_0015955.jpg -- jaccard index: 0.7609474062919617\n",
      "ISIC_0015962.jpg -- jaccard index: 0.8963334560394287\n",
      "ISIC_0015963.jpg -- jaccard index: 0.9162863492965698\n",
      "ISIC_0015968.jpg -- jaccard index: 0.5976202487945557\n",
      "ISIC_0015969.jpg -- jaccard index: 0.37208011746406555\n",
      "ISIC_0015971.jpg -- jaccard index: 0.8731534481048584\n",
      "ISIC_0015973.jpg -- jaccard index: 0.9255213141441345\n",
      "ISIC_0015974.jpg -- jaccard index: 0.8865411877632141\n",
      "ISIC_0015980.jpg -- jaccard index: 0.926950991153717\n",
      "ISIC_0015981.jpg -- jaccard index: 0.9488257169723511\n",
      "ISIC_0015998.jpg -- jaccard index: 0.8211197853088379\n",
      "ISIC_0016000.jpg -- jaccard index: 0.59624844789505\n",
      "ISIC_0016003.jpg -- jaccard index: 0.8572231531143188\n",
      "ISIC_0016012.jpg -- jaccard index: 0.7726336717605591\n",
      "ISIC_0016024.jpg -- jaccard index: 0.43895140290260315\n",
      "ISIC_0016025.jpg -- jaccard index: 0.6813482642173767\n",
      "ISIC_0016028.jpg -- jaccard index: 0.4226078391075134\n",
      "ISIC_0016029.jpg -- jaccard index: 0.44167590141296387\n",
      "ISIC_0016037.jpg -- jaccard index: 0.9020467400550842\n",
      "ISIC_0016040.jpg -- jaccard index: 0.8163691163063049\n",
      "ISIC_0016044.jpg -- jaccard index: 0.8281232714653015\n",
      "ISIC_0016045.jpg -- jaccard index: 0.89387047290802\n",
      "ISIC_0016050.jpg -- jaccard index: 0.8792775273323059\n",
      "ISIC_0016064.jpg -- jaccard index: 0.8336364030838013\n",
      "ISIC_0016065.jpg -- jaccard index: 0.8472983837127686\n",
      "ISIC_0016066.jpg -- jaccard index: 0.8101207613945007\n"
     ]
    }
   ],
   "source": [
    "# Generate Jaccard Index using test set\n",
    "model.load_weights('unet/20190526/unet_lesion_20190526_180-0.83202.hdf5')\n",
    "predictValidationSet(model,224,224)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training co-effiency    : 0.9701533100399135;\n",
      "Validation co-effiency : 0.818605121356159\n"
     ]
    }
   ],
   "source": [
    "#This code combined 2 seperate training history together and plot the result\n",
    "hist = {}\n",
    "for i in history.history.keys():\n",
    "    hist_concate = np.array([np.array(history.history[i]), np.array(history2.history[i])]).flatten() \n",
    "    hist[i] = hist_concate\n",
    "\n",
    "plotTrainigGraph(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
